scholarly journals Hydrological modelling of glacierized catchments focussing on the validation of simulated snow patterns – applications within the flood forecasting system of the Tyrolean river Inn

2010 ◽  
Vol 27 ◽  
pp. 99-109 ◽  
Author(s):  
J. Schöber ◽  
S. Achleitner ◽  
R. Kirnbauer ◽  
F. Schöberl ◽  
H. Schönlaub
Keyword(s):  
Flood Forecasting ◽  
Parameter Setting ◽  
Operational Mode ◽  
Distributed Energy ◽  
Remotely Sensed Data ◽  
Spatially Distributed ◽  
First Results ◽  
Forecasting System ◽  
Energy Balance Approach ◽  
Flood Flow

Abstract. The catchment of the river Inn is located in the Swiss and Austrian Alps. In the frame of the flood forecasting system "HoPI" (Hochwasserprognose für den Tiroler Inn), the Austrian part of the river Inn and its tributaries are covered within a hybrid numerical model. The runoff from the glacierized headwaters of the south-western Inn tributaries is calculated using the Snow- and Icemelt Model "SES" which utilizes a spatially-distributed energy balance approach; within SES, the accumulation and melting processes for snow, firn, and ice are considered. It is of great importance that such a type of model is used in the simulation of alpine areas since in these regions stream flow is influenced by the accumulation and melt of snow and ice and snow-free glaciers have also the potential to increase or even induce flood flow. For a prototype of the forecast system, SES was calibrated using the snow depletion of a glacier, but later, following the first results during the operational mode, the model was recalibrated and validated using remotely-sensed data covering all 13 glacierized catchments. Using the final snow-parameter setting, a simulation run of 15 hydrological years without any state corrections achieved overall agreements between observed and simulated snow cover ranging from 68% to 88% for all individual catchments. Runoff was calibrated and validated using the data from three different gauges. A parameter set, including both validated snow and runoff parameters, was applied for the modelling of a fourth gauged catchment and also achieved accurate results. This final unique parameterization was transferred to the remaining, ungauged watersheds.

scholarly journals Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

2018 ◽  
Vol 18 (8) ◽  
pp. 2183-2202 ◽  
Author(s):  
Ekrem Canli ◽  
Martin Mergili ◽  
Benni Thiebes ◽  
Thomas Glade
Keyword(s):  
High Performance ◽  
Weather Forecasting ◽  
Flood Forecasting ◽  
Ensemble Prediction ◽  
Operational Mode ◽  
Physically Based ◽  
Landslide Forecasting ◽  
Forecasting System ◽  
Prediction Systems ◽  
Performance Computing

Abstract. Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslide-triggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.

scholarly journals Pre-Operational Application of a WRF-Hydro-Based Fluvial Flood Forecasting System in the Southeast Mediterranean

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 437-446
Author(s):  
Christos Giannaros ◽  
Elissavet Galanaki ◽  
Vassiliki Kotroni ◽  
Konstantinos Lagouvardos ◽  
Christina Oikonomou ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Weather Prediction ◽  
Warning System ◽  
Flood Forecasting ◽  
Numerical Weather Prediction Model ◽  
Operational Mode ◽  
Related Information ◽  
Modeling Methodology ◽  
Forecasting System ◽  
Warning Services

The Southeast Mediterranean (SEM) is characterized by increased vulnerability to river/stream flooding. However, impact-oriented, operational fluvial flood forecasting is far away from maturity in the region. The current paper presents the first attempt at introducing an operational impact-based warning system in the area, which is founded on the coupling of a state-of-the-art numerical weather prediction model with an advanced spatially-explicit hydrological model. The system’s modeling methodology and forecasting scheme are presented, as well as prototype results, which were derived under a pre-operational mode. Future developments and challenges needed to be addressed in terms of validating the system and increasing its efficiency are also discussed. This communication highlights that standard approaches used in operational weather forecasting in the SEM for providing flood-related information and alerts can, and should, be replaced by advanced coupled hydrometeorological systems, which can be implemented without a significant cost on the operational character of the provided services. This is of great importance in establishing effective early warning services for fluvial flooding in the region.

scholarly journals A pan-African medium-range ensemble flood forecast system

2015 ◽  
Vol 19 (8) ◽  
pp. 3365-3385 ◽  
Author(s):  
V. Thiemig ◽  
B. Bisselink ◽  
F. Pappenberger ◽  
J. Thielen
Keyword(s):  
Large Scale ◽  
Practical Implication ◽  
River Basins ◽  
Flood Forecasting ◽  
Small Scale ◽  
Flood Forecast ◽  
Flood Events ◽  
Forecast System ◽  
Pan African ◽  
Forecasting System

Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.

Development of a Real-Time Flood Forecasting System by Hydraulic Flood Routing

2001 ◽  
Author(s):  
Joo Heon Lee ◽  
Do Hun Lee ◽  
Sang Man Jeong ◽  
Eun Tae Lee
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Flood Routing ◽  
Forecasting System

Measuring the skill of an operational ice jam flood forecasting system

2021 ◽  
Vol 52 ◽  
pp. 102001
Author(s):  
Brandon S. Williams ◽  
Apurba Das ◽  
Peter Johnston ◽  
Bin Luo ◽  
Karl-Erich Lindenschmidt
Keyword(s):  
Flood Forecasting ◽  
Ice Jam ◽  
Forecasting System

scholarly journals A Robust Skewed Boxplot for Detecting Outliers in Rainfall Observations in Real-Time Flood Forecasting

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Chao Zhao ◽  
Jinyan Yang
Keyword(s):  
Real Time ◽  
Exponential Function ◽  
Flood Forecasting ◽  
Interquartile Range ◽  
Skewed Data ◽  
Symmetric Distributions ◽  
Contaminated Data ◽  
Forecasting System

The standard boxplot is one of the most popular nonparametric tools for detecting outliers in univariate datasets. For Gaussian or symmetric distributions, the chance of data occurring outside of the standard boxplot fence is only 0.7%. However, for skewed data, such as telemetric rain observations in a real-time flood forecasting system, the probability is significantly higher. To overcome this problem, a medcouple (MC) that is robust to resisting outliers and sensitive to detecting skewness was introduced to construct a new robust skewed boxplot fence. Three types of boxplot fences related to MC were analyzed and compared, and the exponential function boxplot fence was selected. Operating on uncontaminated as well as simulated contaminated data, the results showed that the proposed method could produce a lower swamping rate and higher accuracy than the standard boxplot and semi-interquartile range boxplot. The outcomes of this study demonstrated that it is reasonable to use the new robust skewed boxplot method to detect outliers in skewed rain distributions.

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